To train and evaluate our vehicle counting solution, we exploited two public collections of images widely employed in the literature: the CARPK and the PUCPR+ datasets, introduced in [1]}. The CARPK dataset is the first large-scale aerial dataset for counting cars in parking lots. It includes 989 and 459 training and test samples, respectively, each having resolution of \(720 \times 1,280\) . The training images are taken from three different parking lot scenes, while the test set is taken from a fourth scene. The total number of car instances is 42,274 in the range [1,87] (i.e., from a minimum of one car up to a maximum of 87 vehicles in the whole lot) and in the test dataset is 47,500 in the range [2,188]. The PUCPR+ dataset is published in the same paper as the CARPK dataset. It is a subset of the PUCPR dataset introduced in [2]}, adapted by the authors for the counting task. It contains images captured using a fixed camera from a height of the 10th floor of a building, which provides a slanted view of the parking lot. This dataset has 100 and 25 training and test samples, respectively. Images are taken under three different weather conditions (sunny, rainy, and cloudy), resulting in different scene illuminations. The total number of car instances in the training dataset is 1,299 in the range [0,331] and in the test dataset is 3,920 in the range [1,328]. Some sample images from the dataset are shown in fig:carpkpucprplusexample.
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